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A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR

This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very...

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Detalles Bibliográficos
Autores principales: Cavallo, Francesca Romana, Mirza, Khalid Baig, de Mateo, Sara, Miglietta, Luca, Rodriguez-Manzano , Jesus, Nikolic, Konstantin, Toumazou, Christofer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313270/
https://www.ncbi.nlm.nih.gov/pubmed/35884340
http://dx.doi.org/10.3390/bios12070537
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author Cavallo, Francesca Romana
Mirza, Khalid Baig
de Mateo, Sara
Miglietta, Luca
Rodriguez-Manzano , Jesus
Nikolic, Konstantin
Toumazou, Christofer
author_facet Cavallo, Francesca Romana
Mirza, Khalid Baig
de Mateo, Sara
Miglietta, Luca
Rodriguez-Manzano , Jesus
Nikolic, Konstantin
Toumazou, Christofer
author_sort Cavallo, Francesca Romana
collection PubMed
description This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.
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spelling pubmed-93132702022-07-26 A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR Cavallo, Francesca Romana Mirza, Khalid Baig de Mateo, Sara Miglietta, Luca Rodriguez-Manzano , Jesus Nikolic, Konstantin Toumazou, Christofer Biosensors (Basel) Article This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings. MDPI 2022-07-19 /pmc/articles/PMC9313270/ /pubmed/35884340 http://dx.doi.org/10.3390/bios12070537 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cavallo, Francesca Romana
Mirza, Khalid Baig
de Mateo, Sara
Miglietta, Luca
Rodriguez-Manzano , Jesus
Nikolic, Konstantin
Toumazou, Christofer
A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title_full A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title_fullStr A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title_full_unstemmed A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title_short A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR
title_sort point-of-care device for fully automated, fast and sensitive protein quantification via qpcr
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313270/
https://www.ncbi.nlm.nih.gov/pubmed/35884340
http://dx.doi.org/10.3390/bios12070537
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